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GPU utilization tells you how busy an accelerator was, not whether it produced enough useful work for the energy, capacity and money consumed. Measure efficiency by defining a workload and an acceptable quality and latency target, recording completed output and service metrics, and dividing that useful result by energy within a declared system boundary. Then add utilization, queueing, idle capacity, cost and facility measures to explain the result.
This approach works for inference and training, but the examples below focus on serving because inference output, latency and energy can be tied directly to completed requests. A fair comparison keeps the model, quality target, request pattern, concurrency, software stack, measurement interval and energy boundary the same.
The measurement model: useful work divided by resource use
Start with an outcome, not a hardware counter. For a text-generation service, useful work can be completed requests or output tokens produced while the service meets its quality and latency objective. For image, speech, recommendation or classification inference, define the completed task and the quality threshold that makes its result acceptable.
A compact model is:
Efficiency = useful output that meets the target ÷ resource consumed
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The resource may be joules, watt-hours, dollars, water, rack capacity or a combination. The numerator and denominator must cover the same interval and the same requests. If a run generates more tokens but violates the time-to-first-token objective, those tokens are not automatically useful throughput. If a server reports 95% GPU utilization while requests wait in a queue, the busy accelerator has not demonstrated good service efficiency.
Use several views rather than one universal score:
- Outcome view: successful requests, output tokens or completed tasks per joule.
- Service view: throughput that remains inside the latency or service-level objective (SLO).
- Capacity view: accelerator utilization, queueing, idle and reserved machines, and headroom.
- Economic view: cost per request or per million tokens with price, utilization and amortization assumptions exposed.
- Facility view: power-usage effectiveness (PUE), water-usage effectiveness (WUE) and, where required, emissions.
These views answer different questions. Do not replace an outcome metric with utilization, or a workload metric with PUE.
A five-step measurement recipe
1. Define the workload and the acceptance target
Write down exactly what is being served before collecting numbers. Record the model version, tokenizer, precision, batching policy, prompt and output-length distributions, arrival pattern, concurrency, sampling settings and any retrieval or tool calls. State the quality target: for example, an accuracy threshold, an evaluation score, an image similarity requirement or a human-acceptance rule.
For text generation, capture at least:
- Completed requests and output tokens during a fixed interval.
- Time to first token (TTFT).
- Time per output token (TPOT), or the latency measure used by your SLO.
- Request errors, cancellations and responses rejected by the quality gate.
- Input tokens if they affect compute or billing, while keeping input and output tokens separate.
Raw tokens per second is only a capacity signal unless the quality and latency conditions are also met. For another inference task, substitute the task’s completed outputs and quality score. Keep the acceptance test identical when comparing configurations.
MLPerf Inference illustrates why context matters: each scenario specifies a request pattern, dataset and quality target, and reports metrics appropriate to that scenario. A number from one scenario is not interchangeable with a number from another.
2. Declare the system boundary
Choose what the denominator includes and write it in the result title or table. At minimum, distinguish:
- Accelerator energy measured at the device or board.
- Host CPU, memory and local storage energy.
- Networking and external storage, if measured.
- Provisioned machines that are idle, held for failover or waiting for work.
- Power-delivery, cooling and other data-center overhead.
- Water consumption and emissions, when the reporting question requires them.
A GPU-only result is a component measurement. It becomes a whole-service result only when the boundary says so and the missing components are either measured or explicitly excluded. Google’s published serving methodology includes achieved accelerator utilization, idle machines held for availability or failover, CPU and RAM, data-center overhead and water; Google describes active-accelerator-only accounting as an optimistic, non-comprehensive view.
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Use one measurement window and align all streams to a common clock. The window should be long enough to include normal batching, traffic bursts, autoscaling and any warm-up behavior you intend to represent. Record start and end timestamps, request identifiers or aggregate counters, and the configuration hash.
Collect these signals at the finest reliable interval your system supports:
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- Completed and failed requests, input tokens and output tokens.
- TTFT, TPOT, end-to-end latency and queue time, preferably with p50, p95 and p99.
- Accelerator power, energy counter readings, memory use, clocks, temperature and utilization.
- CPU utilization and package power, host memory, network and storage activity.
- Number of active, warming, reserved and idle instances.
- Rack or facility power when available, plus cooling or water data at the chosen boundary.
Take a baseline reading before the run and an ending reading afterward. For cumulative energy counters, subtract the baseline from the ending value and check for counter resets or wraparound. For sampled power, integrate power over time; document the sample rate and whether the integration includes gaps.
NVIDIA’s AIPerf documentation names energy per request, energy per output token, energy per total token, output tokens per joule, performance per watt and goodput per watt. Those metrics require GPU telemetry, and an individual measurement may be omitted when the collector does not expose the necessary signal. Treat the names as metric families, not as proof that every fleet can report them completely.
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Calculate both output-normalized and request-normalized values. Let E be joules measured inside the declared boundary, R completed requests that passed the quality gate, and Tout accepted output tokens.
- Energy per request:
E ÷ R. - Energy per output token:
E ÷ Tout. - Requests per joule:
R ÷ E. - Output tokens per joule:
Tout ÷ E. - Average useful throughput:
RorToutdivided by elapsed seconds, counting only accepted work.
Report the denominator and the population. “Energy per token” is ambiguous unless you say output token, total token, accepted token or generated token including failed requests. If output lengths differ between runs, output-token normalization prevents a longer response from looking like a simple request-rate improvement, while request-normalized energy shows the user-facing cost.
5. Compare configurations at matched conditions
Run the same workload against each configuration with the same quality and SLO gates. Keep hardware generation, software versions, driver and runtime settings, model weights, batching, quantization, concurrency and arrival distribution visible in the report. Use repeated runs or a long enough trace to capture normal variance.
Do not compare an unloaded latency test with a saturated throughput test. If one configuration uses more batching, disclose the changed queueing and latency distribution. If autoscaling changes the number of instances, include the energy of instances that were provisioned but idle within the boundary.
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Choose metrics for the question you need to answer
| Question | Primary metric | What to disclose | What it cannot prove alone |
|---|---|---|---|
| How much accepted work did the system deliver? | Accepted requests per joule or output tokens per joule | Quality gate, output definition, energy boundary and interval | Whether latency targets were met unless the numerator is SLO-qualified |
| Did the service become more energy intensive? | Wh or joules per request, output token or completed task | Input/output mix, response length and failed-request treatment | Capacity available during peaks |
| How much throughput is usable for customers? | Goodput per watt | TTFT and TPOT targets, percentile or window, and power boundary | Quality unless quality is part of the acceptance gate |
| Why is capacity underused? | Utilization, queue time, idle and reserved share | Sampling method, autoscaling state and time period | Useful output or energy efficiency |
| What does service cost? | Cost per request or million tokens | Electricity price, hardware amortization, utilization, staffing and facility allocation | A universal physical-efficiency ranking |
| How much overhead does the facility add? | PUE and WUE | Geography, climate, reporting period, ownership boundary and calculation method | Useful AI output per unit of energy or water |
Goodput is throughput constrained by service quality. NVIDIA describes goodput as throughput achieved while maintaining target TTFT and TPOT levels. Attribute that definition to NVIDIA and state the targets used in your own run; a goodput number without its latency limits is not reproducible.
Understanding utilization without overvaluing it
What high utilization can mean
High accelerator utilization may indicate that kernels are keeping compute units occupied, that batching is effective or that the service is near a throughput limit. It is useful for finding bottlenecks, sizing capacity and detecting unexpected idle periods. Pair it with memory bandwidth, power, clocks, queue time and output rate to determine why the accelerator is busy.
What high utilization can hide
- A workload can spend power on speculative, rejected or low-quality outputs.
- Long queues can keep devices busy while user latency violates the SLO.
- Small requests can produce high utilization but poor output tokens per joule.
- Provisioned failover machines can consume energy even when active devices show excellent utilization.
- Host CPU, RAM, networking and cooling can dominate at low batch sizes.
Conversely, lower utilization can be intentional. A latency-sensitive service may leave headroom to avoid queueing, and a bursty workload may require warm capacity. Judge the resulting accepted work and latency, then use utilization to explain the operating point.
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Set a complete energy boundary
Accelerator and host accounting
Device counters are often the easiest starting point, but they can omit board components, host energy and gaps in telemetry. AIPerf notes that energy may come from a vendor counter or from integrating sampled power. In a mixed-generation fleet, some devices may expose a counter while others do not, making a fleet total incomplete unless the coverage is checked and reported.
For each run, record the measurement method per component: counter name or power-gauge location, sampling interval, calibration status, missing intervals and whether values are measured, estimated or allocated. Do not silently combine a GPU counter with a rack meter and call the result “GPU energy.”
Idle and reserved capacity
Count machines that are powered and provisioned but not processing the measured requests when the question is production efficiency. Separate active energy from idle energy so operators can see whether the remedy is better kernels, better scheduling or less over-provisioning. Availability replicas and failover pools are operational choices; excluding them may be appropriate for a tightly scoped accelerator test, but not for a fleet footprint report.
Facility overhead
When only IT power is measured, label the result as IT or equipment energy. If the report is intended to represent facility consumption, apply the documented facility accounting rather than multiplying by an unexplained factor. Include power distribution and cooling according to the site’s method, and keep water and emissions as separate columns when they are available.
PUE and WUE: useful context, not workload efficiency
Microsoft defines PUE as total facility energy divided by energy used for computing, and WUE as annual cooling and humidification water divided by annual IT energy. These ratios describe overhead intensity at a facility boundary; they do not say how many accepted tokens or tasks were produced.
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|---|---|---|
| Google global fleet average PUE: 1.09 | Google Data Centers, 2025; worldwide fleet during stable operation | Use as Google’s reported fleet context, not as a universal data-center constant |
| Microsoft global PUE: 1.17; WUE: 0.27 L/kWh | FY25, July 1, 2024 through June 30, 2025; fully owned and controlled facilities operational for twelve months at calculation time | Keep the reporting period, ownership and operating boundary with the ratios |
Climate, cooling design, water accounting and geography change interpretation. Compare PUE or WUE only when the definitions and periods align, and pair each ratio with workload-level energy and output. Google also reports more than three times as much compute performance per energy unit in 2025 as five years earlier, based on its internal estimate for comparable 2020 and 2025 CPU, GPU and TPU work; that is an internal fleet estimate, not an industry-wide benchmark.
What published AI energy figures do—and do not—mean
Google Cloud reported on August 21, 2025 that a median Gemini Apps text prompt used an estimated 0.24 Wh of energy, 0.03 gCO2e and 0.26 mL of water under its comprehensive methodology. Google also reported an alternative 0.10 Wh, 0.02 gCO2e and 0.12 mL estimate using active TPU/GPU energy only, and called that calculation non-comprehensive and optimistic because it excludes broader operating factors.
Those figures are estimates for Google’s median Gemini text prompt and methodology. They are not constants for every prompt, model, provider or data center. Prompt length, output length, model routing, hardware, utilization, idle capacity, facility overhead and accounting choices can all change the result.
Google’s Amin Vahdat, SVP and Chief Technologist for AI and Infrastructure, and Jeff Dean, Chief Scientist at Google DeepMind and Google Research, wrote: “Understanding AI’s footprint is crucial, yet thorough data on the energy and environmental impact of AI inference — the use of a trained AI model to make predictions or generate text or images — has been limited.” The statement was published August 21, 2025. Use such provider figures as attributed, methodology-specific examples rather than ranking providers.
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Goodput, cost and Pareto operating points
Latency, throughput, energy and cost often move in opposite directions. Increasing batch size can improve tokens per joule while increasing queue time. Lowering precision can reduce energy but may affect quality. Keeping more replicas online can reduce tail latency while worsening idle energy. A single “winner” hides these choices.
Plot feasible operating points with at least:
- Accepted throughput on the horizontal axis.
- Energy per accepted token or request on the vertical axis.
- Latency percentiles, cost and quality as labels or additional dimensions.
A configuration is dominated when another configuration delivers equal or greater accepted throughput, equal or better latency and quality, and lower energy or cost. The remaining points form a Pareto frontier. Select the point that satisfies the product SLO and capacity plan, not the point with the highest isolated utilization.
NVIDIA’s July 2026 discussion makes this workload-dependence argument in a vendor-authored comparison: different workloads favor different latency, throughput or cost operating points, so it presents Pareto curves rather than one universal result. Any product comparison based on that material should retain NVIDIA’s model, platform, software, workload and test context.
An illustrative calculation
The following numbers are examples for showing the method, not a benchmark result. Suppose two configurations serve the same model and quality gate for one hour. Configuration A accepts 18,000 requests and 1.8 million output tokens while the declared IT boundary measures 7.2 kWh. Configuration B accepts 20,000 requests and 2.0 million output tokens while measuring 8.0 kWh.
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| Metric | Configuration A | Configuration B |
|---|---|---|
| Energy per request | 7,200 Wh ÷ 18,000 = 0.40 Wh | 8,000 Wh ÷ 20,000 = 0.40 Wh |
| Energy per output token | 7,200 Wh ÷ 1,800,000 = 0.004 Wh | 8,000 Wh ÷ 2,000,000 = 0.004 Wh |
| Output tokens per Wh | 250 | 250 |
On this deliberately simple workload, B provides more accepted work at the same normalized energy. That conclusion changes if B misses the TTFT SLO, fails more quality checks, or excludes idle replicas that A includes. Add those conditions before declaring an improvement.
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Store one run manifest with the configuration and one time series with measurements. A practical manifest includes:
- Model and build identifier, tokenizer, precision and serving runtime.
- Hardware type and count, driver and firmware versions.
- Request trace summary: arrival rate, concurrency, prompt and output distributions.
- Quality test version and pass threshold.
- TTFT, TPOT and end-to-end latency targets, including percentile definitions.
- Energy boundary, meters or counters used, sampling rate and missing-data policy.
- Whether idle, failover, host, network, cooling, water and emissions are included.
At each timestamp, retain power or energy, accelerator and host utilization, memory use, queue depth, active and idle instance counts, completed outputs and latency aggregates. Keep raw readings long enough to audit aggregate results. If privacy rules prevent storing request text, store token counts and anonymized request identifiers instead.
Calculate confidence intervals or run-to-run variation when the workload is stochastic. A small difference in energy per token may be noise from request mix or clock behavior. Report the number of repetitions, warm-up treatment and whether the interval contains autoscaling events.
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Only GPU utilization is available
Use utilization as a diagnostic and label the result accordingly. Add request counters, output tokens and latency first. If power telemetry is unavailable, do not invent joules; report the missing signal and use utilization, throughput and latency as an interim capacity view.
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The collector drops energy readings
Check counter support by accelerator generation, permissions, reset events and sampling gaps. Reconcile device totals with a host or rack meter for a test interval. Mark incomplete runs instead of silently treating missing samples as zero.
Energy per request falls because responses got shorter
Report output tokens per joule and quality together with energy per request. Keep input and output distributions matched, or explain why the workload changed. A shorter answer is not an efficiency gain if it fails the required quality target.
Throughput rises but users see slower responses
Apply the TTFT and TPOT SLO before counting goodput. Inspect queue time and latency percentiles, not only average throughput. Reduce batching or concurrency, add capacity, or select a different operating point if the SLO is violated.
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Two teams report incompatible numbers
Diff their manifests. The usual causes are different model versions, request patterns, quality gates, measurement windows, telemetry boundaries, idle-capacity treatment or facility allocation. Re-run with a shared manifest before comparing ratios.
A facility ratio is mistaken for model efficiency
Keep PUE and WUE in a facility section and publish workload output and energy beside them. A low PUE can coexist with an inefficient workload, and a higher PUE site may deliver more accepted output per total joule for a particular service.
Using standardized benchmarks responsibly
MLPerf’s Closed division is designed for apples-to-apples comparisons using the same reference model, while the Open division permits a different model or retraining. A submitted result applies to its scenario, dataset, quality target, hardware and software configuration. The MLPerf results page also warns that results can be changed or invalidated, so preserve the result-set date and submission details when citing a leaderboard number.
Standardized benchmarks are useful for controlled comparison, but production decisions need a trace that resembles your traffic. Reproduce your prompt lengths, arrival bursts, concurrency, SLO and facility boundary, then use a benchmark to isolate hardware or runtime changes.
A production review checklist
- Is the workload defined by model, task, request mix and quality target?
- Are accepted outputs, failures and cancellations counted consistently?
- Are TTFT, TPOT or task-specific latency targets included in the acceptance rule?
- Do output, latency, power and resource streams share a clock and interval?
- Does the boundary state whether it includes accelerator, host, network, storage, idle capacity and facility overhead?
- Are energy counters or power gauges identified, with coverage and missing data documented?
- Are input tokens, output tokens and total tokens separated?
- Are utilization and queueing used to explain results rather than replace them?
- Are cost assumptions, electricity price, amortization and utilization explicit?
- Are PUE and WUE reported with geography, climate, ownership and period?
- Are comparisons matched for model, quality, request pattern, concurrency, software and measurement duration?
- Are latency, throughput, energy and cost shown as operating points or a Pareto frontier when they trade off?
- Are provider figures attributed to the provider’s methodology instead of generalized?
Bottom line
Measure AI infrastructure efficiency as accepted, quality-compliant work per unit of energy, then explain that result with utilization, queueing, idle capacity, cost and facility overhead. A GPU percentage is valuable telemetry, but only the complete, matched measurement tells you whether the service delivered more useful AI with the resources it consumed.
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